The importance of cognitive systems in the decision-making process of travelers: Application of dual-process theory in the processing of online reviews

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DOI:

https://doi.org/10.25145/j.pasos.2026.24.021

Keywords:

Online Reviews, Dual-Process Theory, Cognitive Systems, Hotels, Machine Learning

Abstract

Based on the Dual-Process Theory (DPT), this article investigated the importance of processing the attributes of online reviews by cognitive systems in the decision-making process of travellers. Transformer neural networks were used for multidimensional textual analysis of 89,290 online reviews (OR). The quantitative research tested the hypotheses using three machine learning algorithms: decision trees, random forest,and XGBoost, through the feature importance technique. The results indicated that systems 1 (heuristic, fast, and intuitive processing) and 2 (systematic, slow, rational processing) operate simultaneously and complementarily in the decision-making process and exhibit an additive effect. Furthermore, system 2 plays a significant role in travellers’ decision-making, indicating that consumers carefully examine the content of ORs before booking a hotel. The research enriches the understanding of the application of DPT in online decision-making processes and expands its scope by proposing the multidimensional analysis of digital platform content. The study also contributes to hotel management by identifying the importance of each dimension of ORs.

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References

Abdel-Maksoud, A., Kamel, H., & Elbanna, S. (2016). Investigating relationships between stakeholders’ pressure, eco-control systems and hotel performance. International Journal of Hospitality Management, 59, 95-104. https://doi.org/10.1016/j.ijhm.2016.09.006

Abedi, R., Costache, R., Shafizadeh-Moghadam, H., & Pham, Q. B. (2022). Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto International, 37(19), 5479-5496. https://doi.org/10.1080/10106049.2021.1920636

Aureliano-Silva, L., Leung, X., & Spers, E. E. (2021). The effect of online reviews on restaurant visit intentions: Applying signaling and involvement theories. Journal of Hospitality and Tourism Technology, 12(4), 672-688. https://doi.org/10.1108/JHTT-06-2020-0143

Boehmke B., & Greenwell B.M. (2019). Hands-on machine learning with R. Boca Raton (FL): CRC Press.

Bortoluzzi, D. A., Lunkes, R. J., Santos, E. A. d., & Mendes, A. C. A. (2020). Effect of online hotel reviews on the relationship between defender and prospector strategies and management controls. International Journal of Contemporary Hospitality Management, 32(12), 3721-3745. https://doi.org/10.1108/IJCHM-04-2020-0297

Breiman, L. (2001). Random forest, 45. Mach Learn, 1.

Breiman L., Friedman J.H., Olshen R.A., Stone C.J. (1984). Classification and regression trees. Belmont (CA): Wadsworth International Group, 432, 151–166.

Casalicchio, G., Molnar, C., & Bischl, B. (2019). Visualizing the feature importance for black box models. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I 18 (pp. 655-670). Springer International Publishing.

Chang, K. C. (2013). How reputation creates loyalty in the restaurant sector. International Journal of Contemporary Hospitality Management, 25(4), 536-557. https://doi.org/10.1108/09596111311322916

Chang, H. H., Fang, P. W., & Huang, C. H. (2016). The impact of on-line consumer reviews on value perception: the dual-process theory and uncertainty reduction. In Web design and development: Concepts, methodologies, tools, and applications (pp. 1498-1524). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-4666-8619-9.ch068

Chatterjee, S., Chaudhuri, R., Vrontis, D., Thrassou, A., Ghosh, S. K., & Chaudhuri, S. (2021). Social customer relationship management factors and business benefits. International Journal of Organizational Analysis, 29(1), 35-58. https://doi.org/10.1108/IJOA-11-2019-1933

Eslami, S. P., Ghasemaghaei, M., & Hassanein, K. (2018). Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems, 113, 32-42. https://doi.org/10.1016/j.dss.2018.06.012

Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of business research, 68(6), 1261-1270. https://doi.org/10.1016/j.jbusres.2014.11.006

Filieri, R., McLeay, F., Tsui, B., & Lin, Z. (2018). Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Information & Management, 55(8), 956-970. https://doi.org/10.1016/j.im.2018.04.010

Ghasemaghaei, M., Eslami, S.P., Deal, K. and Hassanein, K. (2018), "Reviews’ length and sentiment as correlates of online reviews’ ratings", Internet Research, Vol. 28 No. 3, pp. 544-563. https://doi.org/10.1108/IntR-12-2016-0394

Ghosh, T. (2017). Managing negative reviews: the persuasive role of webcare characteristics. Journal of Internet Commerce, 16(2), 148-173. https://doi.org/10.1080/15332861.2017.1305254

Grayot, J. (2019). From selves to systems: on the intrapersonal and intraneural dynamics of decision making. Journal of Economic Methodology, 26(3), 208-227. https://doi.org/10.1080/1350178X.2019.1625213

Grayot, J. D. (2020). Dual process theories in behavioral economics and neuroeconomics: A critical review. Review of Philosophy and Psychology, 11 (1), 105–136. https://doi.org/10.1007/s13164-019-00446-9

Herjanto, H., Amin, M., & Cobanoglu, C. (2025). Should I use ChatGPT travel insurance recommendations? A dual-process theory perspective. International Journal of Consumer Studies. https://doi.org/10.1111/ijcs.70044

Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet?. Journal of Interactive Marketing, 18(1), 38-52. https://doi.org/10.1002/dir.10073

Huang, D., So, K. K. F., Huang, J., & Huang, S. (2025). Exploring the attractiveness of digital human influencers in destination marketing: The allure of two-path meaning transfer. Tourism Management, 110, 105166. https://doi.org/10.1016/j.tourman.2025.105166

Jang, S., Chung, J., & Rao, V. R. (2021). The importance of functional and emotional content in online consumer reviews for product sales: Evidence from the mobile gaming market. Journal of Business Research, 130, 583-593. https://doi.org/10.1016/j.jbusres.2019.09.027

Jeesha, K., & Purani, K. (2021). Webcare as a signal: exhaustive-selective webcare strategy and brand evaluation. European Journal of Marketing, 55(7), 1930-1953. https://doi.org/10.1108/EJM-05-2019-0421

Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In Kahneman, D., & Gilovich, T. (Eds.), Heuristics and biases: The Psychology of Intuitive Judgment, 49–81.

Kahneman, D., & Frederick, S. (2005). A model of heuristic judgment. In Holyoak, K., &Morrison, R. (Eds.), The Cambridge handbook of thinking and reasoning, (pp. 267–293). Cambridge University Press.

Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Judgement under uncertainty: Heuristics and biases, (pp. 3–20). Cambridge University Press.

Kitsios, F., Kamariotou, M., Karanikolas, P., & Grigoroudis, E. (2021). Digital marketing platforms and customer satisfaction: Identifying eWOM using big data and text mining. Applied Sciences, 11(17), 8032. https://doi.org/10.3390/app11178032

Kwon, W., Lee, M., Back, K. J., & Lee, K. Y. (2021). Assessing restaurant review helpfulness through big data: dual-process and social influence theory. Journal of Hospitality and Tourism Technology, 12(2), 177-195. https://doi.org/10.1108/JHTT-04-2020-0077

Lawrie, E., Flus, M., Olechowski, A., Hay, L., & Wodehouse, A. (2024). From theory to practice: a roadmap for applying dual-process theory in design cognition research. Journal of Engineering Design, 1-21. https://doi.org/10.1080/09544828.2024.2336837

Liu, Y. and Hu, H.-f. (2021), "Online review helpfulness: the moderating effects of review comprehensiveness", International Journal of Contemporary Hospitality Management, Vol. 33 No. 2, pp. 534-556. https://doi.org/10.1108/IJCHM-08-2020-0856

Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140-151. https://doi.org/10.1016/j.tourman.2014.09.020

Lunkes, R. J., Bortoluzzi, D. A., Anzilago, M., & da Rosa, F. S. (2020). Influence of online hotel reviews on the fit between strategy and use of management control systems: A study among small-and medium-sized hotels in Brazil. Journal of Applied Accounting Research, 21(4), 615-634. https://doi.org/10.1108/JAAR-06-2018-0090

Lunkes, R. J., Deggau, L., Codesso, M.M., Rosa, F. S., & Monteiro, J.J. (2025). The Influence of Online Reviews and Hotel Digital Responsibility on ESG Practices and Sustainability Performance. International Journal of Contemporary Hospitality Management, ahead-of-print, ahead-of-print.

Mandolfo, M., Bettiga, D., Lamberti, L., & Noci, G. (2022). Influence of sales promotion on impulse buying: A dual process approach. Journal of Promotion Management, 28(8), 1212-1234. https://doi.org/10.1080/10496491.2022.2060415

Meek, S., Wilk, V., & Lambert, C. (2021). A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews. Journal of Business Research, 125, 354-367. https://doi.org/10.1016/j.jbusres.2020.12.001

Mohaghegh, M., & Größler, A. (2020). The dynamics of operational problem-solving: A dual-process approach. Systemic Practice and Action Research, 33(1), 27-54. https://doi.org/10.1007/s11213-019-09513-9

Nicolau, J.L., Xiang, Z. and Wang, D. (2024), "Daily online review sentiment and hotel performance", International Journal of Contemporary Hospitality Management, Vol. 36 No. 3, pp. 790-811. https://doi.org/10.1108/IJCHM-05-2022-0594

Paget, S. (2024). 2024 local consumer review survey. Brightlocal. Acesso em 28 de Março de 2024, de https://www.brightlocal.com/research/local-consumer-review-survey/.

Parikh, A. A., Behnke, C., Almanza, B., Nelson, D., & Vorvoreanu, M. (2017). Comparative content analysis of professional, semi-professional, and user-generated restaurant reviews. Journal of Foodservice Business Research, 20(5), 497-511. https://doi.org/10.1080/15378020.2016.1219170

Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers’ hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605–611. https://doi.org/10.1080/13683500.2018.1549025

Rosillo-Díaz, E., Muñoz-Rosas, J. F., & Blanco-Encomienda, F. J. (2024). Impact of heuristic–systematic cues on the purchase intention of the electronic commerce consumer through the perception of product quality. Journal of Retailing and Consumer Services, 81, 103980. https://doi.org/10.1016/j.jretconser.2024.103980

Roy, G. (2023). Travelers’ online review on hotel performance–Analyzing facts with the Theory of Lodging and sentiment analysis. International Journal of Hospitality Management, 111, 103459. https://doi.org/10.1016/j.ijhm.2023.103459

Saarela, M., & Jauhiainen, S. (2021). Comparison of feature importance measures as explanations for classification models. SN Applied Sciences, 3(2), 272. https://doi.org/10.1007/s42452-021-04148-9

Shin, H. W., Fan, A., & Wu, L. (2022). Trust the Facts: The Impact of Reviews’ Written Style and Subject-Focus on Peer-to-Peer Accommodation Consumption. Journal of Hospitality & Tourism Research, 48(2), 249-276. https://doi.org/10.1177/10963480221100244 (Original work published 2024)

Srivastava, V., & Kalro, A. D. (2019). Enhancing the helpfulness of online consumer reviews: the role of latent (content) factors. Journal of Interactive Marketing, 48(1), 33-50. https://doi.org/10.1016/j.intmar.2018.12.003

Standing, C., Holzweber, M., & Mattsson, J. (2016). Exploring emotional expressions in e-word-of-mouth from online communities. Information Processing & Management, 52(5), 721-732. https://doi.org/10.1016/j.ipm.2016.01.001

Steur, A. J., Fritzsche, F., & Seiter, M. (2022). It’s all about the text: An experimental investigation of inconsistent reviews on restaurant booking platforms. Electronic Markets, 1-34. https://doi.org/10.1007/s12525-022-00525-3

TripAdvisor (2024). https://tripadvisor.mediaroom.com/US-about-us Acesso em 05 de março de 2024.

Tversky, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology, 5(2), 207-232.

van Noort, G., & Willemsen, L. M. (2012). Online damage control: The effects of proactive versus reactive webcare interventions in consumer-generated and brand-generated platforms. Journal of Interactive Marketing, 26(3), 131-140. https://doi.org/10.1016/j.intmar.2011.07.001

Wang, Y., Tariq, S., & Alvi, T. H. (2021). How primary and supplementary reviews affect consumer decision making? Roles of psychological and managerial mechanisms. Electronic Commerce Research and Applications, 46, 101032. https://doi.org/10.1016/j.elerap.2021.101032

Wang, Q., Zhang, W., Li, J., Mai, F., & Ma, Z. (2022). Effect of online review sentiment on product sales: The moderating role of review credibility perception. Computers in Human Behavior, 133, 107272. https://doi.org/10.1016/j.chb.2022.107272

Wason, P. C., & Evans, J. S. B. (1974). Dual processes in reasoning?. Cognition, 3(2), 141-154. https://doi.org/10.1016/0010-0277(74)90017-1

Yan, L., & Wang, X. (2018). Why posters contribute different content in their positive online reviews: A social information-processing perspective. Computers in Human Behavior, 82, 199-216. https://doi.org/10.1016/j.chb.2018.01.009

Zhai, M., Wang, X., & Zhao, X. (2024). The importance of online customer reviews characteristics on remanufactured product sales: Evidence from the mobile phone market on Amazon. com. Journal of Retailing and Consumer Services, 77, 103677. https://doi.org/10.1016/j.jretconser.2023.103677

Published

2026-04-29

How to Cite

Donadio Costa, G., João Lunkes, R., & Silva da Rosa, F. (2026). The importance of cognitive systems in the decision-making process of travelers: Application of dual-process theory in the processing of online reviews. PASOS Revista De Turismo Y Patrimonio Cultural, 24(2), 305–320. https://doi.org/10.25145/j.pasos.2026.24.021